Overview

Dataset statistics

Number of variables16
Number of observations2009
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory237.6 KiB
Average record size in memory121.1 B

Variable types

DateTime1
Categorical4
Numeric11

Warnings

username has constant value "richardbranson" Constant
cashtags has constant value "0" Constant
tweet has a high cardinality: 2009 distinct values High cardinality
hashtags is highly correlated with number of tweetsHigh correlation
video is highly correlated with photos and 2 other fieldsHigh correlation
photos is highly correlated with video and 2 other fieldsHigh correlation
urls is highly correlated with video and 2 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
number of tweets is highly correlated with hashtags and 3 other fieldsHigh correlation
video is highly correlated with photos and 5 other fieldsHigh correlation
photos is highly correlated with video and 5 other fieldsHigh correlation
urls is highly correlated with video and 5 other fieldsHigh correlation
replies_count is highly correlated with video and 5 other fieldsHigh correlation
retweets_count is highly correlated with video and 5 other fieldsHigh correlation
likes_count is highly correlated with video and 5 other fieldsHigh correlation
number of tweets is highly correlated with video and 5 other fieldsHigh correlation
video is highly correlated with photos and 4 other fieldsHigh correlation
photos is highly correlated with video and 2 other fieldsHigh correlation
urls is highly correlated with video and 4 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with video and 4 other fieldsHigh correlation
likes_count is highly correlated with video and 4 other fieldsHigh correlation
number of tweets is highly correlated with video and 4 other fieldsHigh correlation
urls is highly correlated with video and 3 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
video is highly correlated with urls and 4 other fieldsHigh correlation
mentions is highly correlated with video and 2 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 2 other fieldsHigh correlation
photos is highly correlated with urls and 3 other fieldsHigh correlation
bins is highly correlated with price and 1 other fieldsHigh correlation
number of tweets is highly correlated with urls and 4 other fieldsHigh correlation
price is highly correlated with urls and 3 other fieldsHigh correlation
hashtags is highly correlated with video and 2 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
percent change is highly correlated with retweets_count and 2 other fieldsHigh correlation
cashtags is highly correlated with bins and 1 other fieldsHigh correlation
bins is highly correlated with cashtags and 1 other fieldsHigh correlation
username is highly correlated with cashtags and 1 other fieldsHigh correlation
retweets_count is highly skewed (γ1 = 27.07167542) Skewed
likes_count is highly skewed (γ1 = 28.92064342) Skewed
tweet is uniformly distributed Uniform
date has unique values Unique
tweet has unique values Unique
mentions has 389 (19.4%) zeros Zeros
hashtags has 678 (33.7%) zeros Zeros
video has 126 (6.3%) zeros Zeros
photos has 211 (10.5%) zeros Zeros
urls has 65 (3.2%) zeros Zeros
percent change has 211 (10.5%) zeros Zeros

Reproduction

Analysis started2021-09-27 19:04:19.085795
Analysis finished2021-09-27 19:04:38.063767
Duration18.98 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

date
Date

UNIQUE

Distinct2009
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Minimum2017-09-29 16:00:00
Maximum2021-07-20 16:00:00
2021-09-27T15:04:38.176534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:38.342265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2009
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Climate change creates huge opportunities for innovation, not least in transportation. Here’s one example https://t.co/yEPnQFirpo #RunOnLess https://t.co/ypbp5RmDZl How to develop a team of leaders: https://t.co/P0qc7kD55K @virginmobilecan #readbyrichard https://t.co/PSKjZgaGoo Exciting project to modernise Saudi Arabia & welcome tourists to beautiful undiscovered country & stunning landscape https://t.co/jIFLJ10wBe https://t.co/GUmn8qxdKJ @apolloxxxx Thank you for your kind words @RdzSaint Forget one, here’s 65 – not all on entrepreneurship, but all useful for an entrepreneur https://t.co/wTT9njdKlh As a kid one of my heroes was Lawrence of Arabia. Treat to see this restored train he blew up in Saudi Arabia https://t.co/jIFLJ10wBe https://t.co/X2SDLsHQU8 @sarah_robes15 Thank you Sarah, that’s lovely to hear. Next up - #FindingMyVirginity @ErranteMec I’m delighted to hear you enjoyed it. My new autobiography is out next month too, so good timing! #FindingMyVirginity Visited incredible UNESCO World Heritage Site, Mada’in Saleh. Privelige to see this awe-inspiring ancient ruin https://t.co/jIFLJ10wBe https://t.co/CtHQ1Rprsf Quite an experience to be in Saudi Arabia on the day women were given the right to drive for the 1st time https://t.co/jIFLJ10wBe https://t.co/mdFeQz4Q94 Just enjoyed a fascinating visit to Saudi Arabia, a country where great change is taking place step by step https://t.co/jIFLJ10wBe
 
1
What makes @Virgin really special is all our dedicated, talented, wonderful people: https://t.co/CT6Knd4qlj #VirginFamily https://t.co/lejArDCis4
 
1
Concerned to read about the new GCSE exam reforms. Due to a crackdown in grammar, students with a dyslexia diagnosis accepted by exam boards are penalised for every mistake https://t.co/H6NcQbYAPE #readbyrichard https://t.co/rLjuNZvPSB Shining a spotlight on some of the most special experiences that can be had through @VirginLimitedEd: https://t.co/fUqz4oyvzw #VirginFamily https://t.co/En71HqlMCC
 
1
Lovely to learn more about some of the team members who are bringing Scarlet Lady, @VirginVoyages' very first ship, to life: https://t.co/8y0LMbAmDe #VirginFamily https://t.co/pehVVB8y03 Here’s an interesting look into why we need to do more to protect the ocean: https://t.co/R8mpAhyFJP @oceanunite #ReadByRichard https://t.co/vZRHYFF1jY
 
1
@virgingalactic's VMS Eve arrives home to Spaceport America – a brilliant view from Chief Pilot Dave Mackay: https://t.co/fWbetBlzqG https://t.co/hZXyS52O4r A compelling analysis highlighting the simple economic logic of investing in clean energy https://t.co/gbhoPSJQHf #readbyrichard
 
1
Other values (2004)
2004 

Length

Max length2702
Median length533
Mean length591.1324042
Min length2

Characters and Unicode

Total characters1187585
Distinct characters114
Distinct categories16 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2009 ?
Unique (%)100.0%

Sample

1st rowClimate change creates huge opportunities for innovation, not least in transportation. Here’s one example https://t.co/yEPnQFirpo #RunOnLess https://t.co/ypbp5RmDZl How to develop a team of leaders: https://t.co/P0qc7kD55K @virginmobilecan #readbyrichard https://t.co/PSKjZgaGoo Exciting project to modernise Saudi Arabia & welcome tourists to beautiful undiscovered country & stunning landscape https://t.co/jIFLJ10wBe https://t.co/GUmn8qxdKJ @apolloxxxx Thank you for your kind words @RdzSaint Forget one, here’s 65 – not all on entrepreneurship, but all useful for an entrepreneur https://t.co/wTT9njdKlh As a kid one of my heroes was Lawrence of Arabia. Treat to see this restored train he blew up in Saudi Arabia https://t.co/jIFLJ10wBe https://t.co/X2SDLsHQU8 @sarah_robes15 Thank you Sarah, that’s lovely to hear. Next up - #FindingMyVirginity @ErranteMec I’m delighted to hear you enjoyed it. My new autobiography is out next month too, so good timing! #FindingMyVirginity Visited incredible UNESCO World Heritage Site, Mada’in Saleh. Privelige to see this awe-inspiring ancient ruin https://t.co/jIFLJ10wBe https://t.co/CtHQ1Rprsf Quite an experience to be in Saudi Arabia on the day women were given the right to drive for the 1st time https://t.co/jIFLJ10wBe https://t.co/mdFeQz4Q94 Just enjoyed a fascinating visit to Saudi Arabia, a country where great change is taking place step by step https://t.co/jIFLJ10wBe
2nd rowMy top ten quotes on inspiring leadership: https://t.co/CZ8XdWSm9z https://t.co/5eQHtxwivM Great to see @VirginMedia taking steps to better support disabled employees: https://t.co/l7Xqv7HhH4 https://t.co/7Utka0OpCP Some wonderful insight on what it means to be a modern leader in a @Virgin company: https://t.co/QFxvQ3VrsP #virginfamily #ReadByRichard https://t.co/alkxhgZZMt Enjoyed a fascinating visit to Saudi Arabia, a country where great change is taking place step by step https://t.co/jIFLJ10wBe https://t.co/MeIdSTqiay One area ripe for innovation is trucking. Yes, trucking. This challenge showed how https://t.co/yEPnQFirpo #RunOnLess https://t.co/BLDhkSXBmx
3rd rowWhile I’m excited for the future, it’s interesting listening to reluctant futurist @benhammersley on #FutureVisions https://t.co/OINjUVo8j8 “Take every opportunity that presents itself” – great advice from CEO Merren McArthur: https://t.co/QFxvQ3VrsP #ReadbyRichard #VirginFamily https://t.co/M7KViw1lxQ So proud of my son @sambranson for making this moving documentary on the #BVI community, Help, hope & hurricanes https://t.co/rqaoJNEvug
4th rowSustainable development advocate @MatsGranryd has joined @thebteamhq: https://t.co/4yZqBeIeUY https://t.co/8wI9eSvGgP While I have given up beef, could smarter grazing actually help reverse global warming? https://t.co/SsRsFltm2r #ReadbyRichard https://t.co/UWJC2xMMZa
5th rowMy top ten quotes on inspiring leadership: https://t.co/CZ8XdWSm9z https://t.co/jIeRYXaHgY Supporting the animal populations of Necker & Moskito Island after the hurricanes: https://t.co/EtiGa2uRPy https://t.co/h7UPgeQCmU Fascinating insight on how gaming can be a force for good from @ian_livingstone and @playmob https://t.co/ICJ7Hm3EMZ #ReadbyRichard

Common Values

ValueCountFrequency (%)
Climate change creates huge opportunities for innovation, not least in transportation. Here’s one example https://t.co/yEPnQFirpo #RunOnLess https://t.co/ypbp5RmDZl How to develop a team of leaders: https://t.co/P0qc7kD55K @virginmobilecan #readbyrichard https://t.co/PSKjZgaGoo Exciting project to modernise Saudi Arabia & welcome tourists to beautiful undiscovered country & stunning landscape https://t.co/jIFLJ10wBe https://t.co/GUmn8qxdKJ @apolloxxxx Thank you for your kind words @RdzSaint Forget one, here’s 65 – not all on entrepreneurship, but all useful for an entrepreneur https://t.co/wTT9njdKlh As a kid one of my heroes was Lawrence of Arabia. Treat to see this restored train he blew up in Saudi Arabia https://t.co/jIFLJ10wBe https://t.co/X2SDLsHQU8 @sarah_robes15 Thank you Sarah, that’s lovely to hear. Next up - #FindingMyVirginity @ErranteMec I’m delighted to hear you enjoyed it. My new autobiography is out next month too, so good timing! #FindingMyVirginity Visited incredible UNESCO World Heritage Site, Mada’in Saleh. Privelige to see this awe-inspiring ancient ruin https://t.co/jIFLJ10wBe https://t.co/CtHQ1Rprsf Quite an experience to be in Saudi Arabia on the day women were given the right to drive for the 1st time https://t.co/jIFLJ10wBe https://t.co/mdFeQz4Q94 Just enjoyed a fascinating visit to Saudi Arabia, a country where great change is taking place step by step https://t.co/jIFLJ10wBe1
 
< 0.1%
What makes @Virgin really special is all our dedicated, talented, wonderful people: https://t.co/CT6Knd4qlj #VirginFamily https://t.co/lejArDCis41
 
< 0.1%
Concerned to read about the new GCSE exam reforms. Due to a crackdown in grammar, students with a dyslexia diagnosis accepted by exam boards are penalised for every mistake https://t.co/H6NcQbYAPE #readbyrichard https://t.co/rLjuNZvPSB Shining a spotlight on some of the most special experiences that can be had through @VirginLimitedEd: https://t.co/fUqz4oyvzw #VirginFamily https://t.co/En71HqlMCC1
 
< 0.1%
Lovely to learn more about some of the team members who are bringing Scarlet Lady, @VirginVoyages' very first ship, to life: https://t.co/8y0LMbAmDe #VirginFamily https://t.co/pehVVB8y03 Here’s an interesting look into why we need to do more to protect the ocean: https://t.co/R8mpAhyFJP @oceanunite #ReadByRichard https://t.co/vZRHYFF1jY1
 
< 0.1%
@virgingalactic's VMS Eve arrives home to Spaceport America – a brilliant view from Chief Pilot Dave Mackay: https://t.co/fWbetBlzqG https://t.co/hZXyS52O4r A compelling analysis highlighting the simple economic logic of investing in clean energy https://t.co/gbhoPSJQHf #readbyrichard1
 
< 0.1%
So excited to announce that @virgingalactic‘s spaceport is now operationally ready to host our remaining test flight program and welcome our very first Future Astronauts https://t.co/jfzCvaTOEu https://t.co/qojMoM4t6K1
 
< 0.1%
@virgingalactic have revealed their Gateway to Space, an area within Spaceport America which features a lounge and spaceflight operations https://t.co/jfzCvaTOEu https://t.co/dcZHhpgC70 It’s so exciting to get the first glimpse inside Spaceport America and see where our journey to space will begin https://t.co/jfzCvaTOEu @virgingalactic https://t.co/VsYIN1mxwA Looking good team! Can’t wait to visit Sydney’s newest Virgin Active Australia club @VirginActiveOz https://t.co/QIRepdGOwJ https://t.co/GG40RNiCtA So lovely to hear that the couple embracing on the cover of the Woodstock soundtrack is still together, 50 years down the track. A nice read https://t.co/ywx4GMPVvH #readbyrichard https://t.co/2kCNjqZVZu Four drug policies proven to save lives https://t.co/a3KYuLga2f https://t.co/ZBxEIExsTI1
 
< 0.1%
The world’s first passenger hyperloop system: https://t.co/2PaVGajm1U @Virgin @HyperloopOne https://t.co/kTcj05ZGG6 @Victor_OCHEN @_thenewnow @UNDPUganda @nikolajcw @RaftoFoundation @ICRC Truly inspiring Victor. Keep up the brilliant work @Blogger_Miriam @SparklesIsFancy @GoBrightline @VirginMiaCntrl @VirginTrains So sweet. Hope you both had a wonderful journey1
 
< 0.1%
Being dyslexic, I always struggled with exams and ended up dropping out of school aged 16. I’m concerned to hear that recent reforms are making it even harder for students with dyslexia to reach their full potential https://t.co/0NaMtGHL4p @MadeByDyslexia #ALevelResultsDay https://t.co/qUcADPvX5E As exam results roll in, remember the alphabet goes from A to Z not A to E, and life is a lot more fun when you colour outside of the lines https://t.co/0NaMtGHL4p @MadeByDyslexia #alevelresults2019 https://t.co/Lfdj0aq0AO It’s results day and many students are weighing up their options for the future. I am often asked about the value of continuing with further education, and here are my thoughts: https://t.co/0NaMtGHL4p @MadeByDyslexia #alevelresultsday https://t.co/YAzOvADsP0 Great to see how this London university is making big steps to reduce its carbon footprint. Hopefully more follow suit https://t.co/EgIrCUAvd5 #readbyrichard https://t.co/Stb1M8HPfn1
 
< 0.1%
@hkinder13 English Breakfast with a splash of milk and no sugar - always hot! @tom21ball Thanks Tom. Really glad you enjoyed it @HollyBranson @Virgin @virgingalactic Thanks Holly – there’s a lot of great memories So proud of the efforts of the teams who have turned the West Coast Main Line around from the struggling franchise it was when @VirginTrains took it over in 1997, to the top-rated service it is today https://t.co/7O7SmQbngd #VirginFamily https://t.co/rSkgdjJ5qw The recent period of uncertainty has been challenging for everyone at @VirginTrains, but I am so very proud of how hard they have all worked to maintain our high standards of customer experience https://t.co/7O7SmQbngd https://t.co/ZaMYxOmbsP Received the news of the DfT's decision that the West Coast Mainline franchise will pass to First Trenitalia. I am devastated for the @VirginTrains teams who have worked tirelessly to become the top-rated franchise by customers in the UK https://t.co/7O7SmQbngd #VirginFamily https://t.co/XO0l8bib5h1
 
< 0.1%
Other values (1999)1999
99.5%

Length

2021-09-27T15:04:38.689717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the6189
 
4.0%
to5422
 
3.5%
a3305
 
2.2%
and3049
 
2.0%
of2903
 
1.9%
in1939
 
1.3%
for1825
 
1.2%
you1488
 
1.0%
is1443
 
0.9%
on1432
 
0.9%
Other values (21083)124121
81.1%

Most occurring characters

ValueCountFrequency (%)
163726
 
13.8%
t95238
 
8.0%
e80052
 
6.7%
o67102
 
5.7%
i58175
 
4.9%
a56329
 
4.7%
n54767
 
4.6%
s53854
 
4.5%
r47748
 
4.0%
h42652
 
3.6%
Other values (104)467942
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter822436
69.3%
Space Separator163726
 
13.8%
Uppercase Letter88045
 
7.4%
Other Punctuation82219
 
6.9%
Decimal Number24317
 
2.0%
Final Punctuation3207
 
0.3%
Dash Punctuation2206
 
0.2%
Connector Punctuation710
 
0.1%
Initial Punctuation365
 
< 0.1%
Close Punctuation102
 
< 0.1%
Other values (6)252
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t95238
11.6%
e80052
 
9.7%
o67102
 
8.2%
i58175
 
7.1%
a56329
 
6.8%
n54767
 
6.7%
s53854
 
6.5%
r47748
 
5.8%
h42652
 
5.2%
c33336
 
4.1%
Other values (21)233183
28.4%
Uppercase Letter
ValueCountFrequency (%)
V5738
 
6.5%
I4909
 
5.6%
T4717
 
5.4%
A4418
 
5.0%
M4365
 
5.0%
S4139
 
4.7%
W4139
 
4.7%
B3911
 
4.4%
C3765
 
4.3%
H3734
 
4.2%
Other values (16)44210
50.2%
Other Punctuation
ValueCountFrequency (%)
/36885
44.9%
.15078
18.3%
:13966
 
17.0%
@7045
 
8.6%
,3345
 
4.1%
#3185
 
3.9%
?636
 
0.8%
!504
 
0.6%
;429
 
0.5%
&394
 
0.5%
Other values (5)752
 
0.9%
Decimal Number
ValueCountFrequency (%)
03463
14.2%
12770
11.4%
22757
11.3%
52357
9.7%
32274
9.4%
92191
9.0%
82176
8.9%
42137
8.8%
72105
8.7%
62087
8.6%
Other Symbol
ValueCountFrequency (%)
🔹4
30.8%
🌟1
 
7.7%
💫1
 
7.7%
🍕1
 
7.7%
👩1
 
7.7%
1
 
7.7%
😊1
 
7.7%
👋1
 
7.7%
🏃1
 
7.7%
😎1
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
-1124
51.0%
1070
48.5%
11
 
0.5%
1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+40
76.9%
|10
 
19.2%
=1
 
1.9%
~1
 
1.9%
Final Punctuation
ValueCountFrequency (%)
2911
90.8%
296
 
9.2%
Initial Punctuation
ValueCountFrequency (%)
293
80.3%
72
 
19.7%
Currency Symbol
ValueCountFrequency (%)
$53
62.4%
£32
37.6%
Open Punctuation
ValueCountFrequency (%)
(99
99.0%
[1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
)101
99.0%
]1
 
1.0%
Space Separator
ValueCountFrequency (%)
163726
100.0%
Connector Punctuation
ValueCountFrequency (%)
_710
100.0%
Format
ValueCountFrequency (%)
1
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin910481
76.7%
Common277102
 
23.3%
Inherited2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t95238
 
10.5%
e80052
 
8.8%
o67102
 
7.4%
i58175
 
6.4%
a56329
 
6.2%
n54767
 
6.0%
s53854
 
5.9%
r47748
 
5.2%
h42652
 
4.7%
c33336
 
3.7%
Other values (47)321228
35.3%
Common
ValueCountFrequency (%)
163726
59.1%
/36885
 
13.3%
.15078
 
5.4%
:13966
 
5.0%
@7045
 
2.5%
03463
 
1.2%
,3345
 
1.2%
#3185
 
1.1%
2911
 
1.1%
12770
 
1.0%
Other values (45)24728
 
8.9%
Inherited
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1182706
99.6%
Punctuation4808
 
0.4%
Latin 1 Sup57
 
< 0.1%
None10
 
< 0.1%
Emoticons2
 
< 0.1%
Dingbats1
 
< 0.1%
VS1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163726
 
13.8%
t95238
 
8.1%
e80052
 
6.8%
o67102
 
5.7%
i58175
 
4.9%
a56329
 
4.8%
n54767
 
4.6%
s53854
 
4.6%
r47748
 
4.0%
h42652
 
3.6%
Other values (78)463063
39.2%
Punctuation
ValueCountFrequency (%)
2911
60.5%
1070
 
22.3%
296
 
6.2%
293
 
6.1%
153
 
3.2%
72
 
1.5%
11
 
0.2%
1
 
< 0.1%
1
 
< 0.1%
Latin 1 Sup
ValueCountFrequency (%)
£32
56.1%
ö9
 
15.8%
é7
 
12.3%
ç6
 
10.5%
ó2
 
3.5%
ü1
 
1.8%
None
ValueCountFrequency (%)
🔹4
40.0%
🌟1
 
10.0%
💫1
 
10.0%
🍕1
 
10.0%
👩1
 
10.0%
👋1
 
10.0%
🏃1
 
10.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
VS
ValueCountFrequency (%)
1
100.0%
Emoticons
ValueCountFrequency (%)
😊1
50.0%
😎1
50.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
richardbranson
2009 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters28126
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrichardbranson
2nd rowrichardbranson
3rd rowrichardbranson
4th rowrichardbranson
5th rowrichardbranson

Common Values

ValueCountFrequency (%)
richardbranson2009
100.0%

Length

2021-09-27T15:04:39.839767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:04:39.915543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
richardbranson2009
100.0%

Most occurring characters

ValueCountFrequency (%)
r6027
21.4%
a4018
14.3%
n4018
14.3%
i2009
 
7.1%
c2009
 
7.1%
h2009
 
7.1%
d2009
 
7.1%
b2009
 
7.1%
s2009
 
7.1%
o2009
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28126
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r6027
21.4%
a4018
14.3%
n4018
14.3%
i2009
 
7.1%
c2009
 
7.1%
h2009
 
7.1%
d2009
 
7.1%
b2009
 
7.1%
s2009
 
7.1%
o2009
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin28126
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r6027
21.4%
a4018
14.3%
n4018
14.3%
i2009
 
7.1%
c2009
 
7.1%
h2009
 
7.1%
d2009
 
7.1%
b2009
 
7.1%
s2009
 
7.1%
o2009
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII28126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r6027
21.4%
a4018
14.3%
n4018
14.3%
i2009
 
7.1%
c2009
 
7.1%
h2009
 
7.1%
d2009
 
7.1%
b2009
 
7.1%
s2009
 
7.1%
o2009
 
7.1%

mentions
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.378795421
Minimum0
Maximum25
Zeros389
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:39.983068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.285492167
Coefficient of variation (CV)0.9607771005
Kurtosis9.869585863
Mean2.378795421
Median Absolute Deviation (MAD)1
Skewness2.082529809
Sum4779
Variance5.223474445
MonotonicityNot monotonic
2021-09-27T15:04:40.127876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1450
22.4%
2416
20.7%
0389
19.4%
3284
14.1%
4168
 
8.4%
5115
 
5.7%
685
 
4.2%
743
 
2.1%
826
 
1.3%
911
 
0.5%
Other values (7)22
 
1.1%
ValueCountFrequency (%)
0389
19.4%
1450
22.4%
2416
20.7%
3284
14.1%
4168
 
8.4%
5115
 
5.7%
685
 
4.2%
743
 
2.1%
826
 
1.3%
911
 
0.5%
ValueCountFrequency (%)
251
 
< 0.1%
202
 
0.1%
161
 
< 0.1%
133
 
0.1%
122
 
0.1%
116
 
0.3%
107
 
0.3%
911
 
0.5%
826
1.3%
743
2.1%

hashtags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.582379293
Minimum0
Maximum36
Zeros678
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:40.244169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum36
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.083493301
Coefficient of variation (CV)1.316683876
Kurtosis53.58231976
Mean1.582379293
Median Absolute Deviation (MAD)1
Skewness4.761415986
Sum3179
Variance4.340944336
MonotonicityNot monotonic
2021-09-27T15:04:40.363088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0678
33.7%
1555
27.6%
2350
17.4%
3178
 
8.9%
4103
 
5.1%
563
 
3.1%
635
 
1.7%
719
 
0.9%
811
 
0.5%
106
 
0.3%
Other values (7)11
 
0.5%
ValueCountFrequency (%)
0678
33.7%
1555
27.6%
2350
17.4%
3178
 
8.9%
4103
 
5.1%
563
 
3.1%
635
 
1.7%
719
 
0.9%
811
 
0.5%
94
 
0.2%
ValueCountFrequency (%)
361
 
< 0.1%
271
 
< 0.1%
201
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
112
 
0.1%
106
 
0.3%
94
 
0.2%
811
0.5%
719
0.9%

cashtags
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
2009 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2009
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02009
100.0%

Length

2021-09-27T15:04:40.600853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:04:40.676033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
02009
100.0%

Most occurring characters

ValueCountFrequency (%)
02009
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02009
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02009
100.0%

video
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.953210553
Minimum0
Maximum36
Zeros126
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:40.742047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum36
Range36
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.033778718
Coefficient of variation (CV)0.6886670224
Kurtosis34.11105516
Mean2.953210553
Median Absolute Deviation (MAD)1
Skewness2.657671621
Sum5933
Variance4.136255872
MonotonicityNot monotonic
2021-09-27T15:04:40.848358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2437
21.8%
3387
19.3%
1380
18.9%
4274
13.6%
5186
9.3%
0126
 
6.3%
6109
 
5.4%
784
 
4.2%
815
 
0.7%
96
 
0.3%
Other values (2)5
 
0.2%
ValueCountFrequency (%)
0126
 
6.3%
1380
18.9%
2437
21.8%
3387
19.3%
4274
13.6%
5186
9.3%
6109
 
5.4%
784
 
4.2%
815
 
0.7%
96
 
0.3%
ValueCountFrequency (%)
361
 
< 0.1%
104
 
0.2%
96
 
0.3%
815
 
0.7%
784
 
4.2%
6109
 
5.4%
5186
9.3%
4274
13.6%
3387
19.3%
2437
21.8%

photos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.762070682
Minimum0
Maximum10
Zeros211
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:40.981648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.926212272
Coefficient of variation (CV)0.6973797901
Kurtosis0.01273807609
Mean2.762070682
Median Absolute Deviation (MAD)1
Skewness0.6567127963
Sum5549
Variance3.710293718
MonotonicityNot monotonic
2021-09-27T15:04:41.089490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2441
22.0%
1367
18.3%
3362
18.0%
4255
12.7%
0211
10.5%
5178
8.9%
696
 
4.8%
775
 
3.7%
815
 
0.7%
97
 
0.3%
ValueCountFrequency (%)
0211
10.5%
1367
18.3%
2441
22.0%
3362
18.0%
4255
12.7%
5178
8.9%
696
 
4.8%
775
 
3.7%
815
 
0.7%
97
 
0.3%
ValueCountFrequency (%)
102
 
0.1%
97
 
0.3%
815
 
0.7%
775
 
3.7%
696
 
4.8%
5178
8.9%
4255
12.7%
3362
18.0%
2441
22.0%
1367
18.3%

urls
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.161772026
Minimum0
Maximum10
Zeros65
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:41.196691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.937317539
Coefficient of variation (CV)0.6127315707
Kurtosis0.0501241763
Mean3.161772026
Median Absolute Deviation (MAD)1
Skewness0.701837995
Sum6352
Variance3.753199249
MonotonicityNot monotonic
2021-09-27T15:04:41.328522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2439
21.9%
3379
18.9%
1367
18.3%
4298
14.8%
5193
9.6%
6125
 
6.2%
7102
 
5.1%
065
 
3.2%
827
 
1.3%
107
 
0.3%
ValueCountFrequency (%)
065
 
3.2%
1367
18.3%
2439
21.9%
3379
18.9%
4298
14.8%
5193
9.6%
6125
 
6.2%
7102
 
5.1%
827
 
1.3%
97
 
0.3%
ValueCountFrequency (%)
107
 
0.3%
97
 
0.3%
827
 
1.3%
7102
 
5.1%
6125
 
6.2%
5193
9.6%
4298
14.8%
3379
18.9%
2439
21.9%
1367
18.3%

replies_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct350
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.0652066
Minimum0
Maximum8817
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:41.602224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q144
median74
Q3125
95-th percentile297.6
Maximum8817
Range8817
Interquartile range (IQR)81

Descriptive statistics

Standard deviation288.5967962
Coefficient of variation (CV)2.345072212
Kurtosis442.3735475
Mean123.0652066
Median Absolute Deviation (MAD)36
Skewness17.28258706
Sum247238
Variance83288.11079
MonotonicityNot monotonic
2021-09-27T15:04:41.830948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4427
 
1.3%
4625
 
1.2%
5325
 
1.2%
5524
 
1.2%
2823
 
1.1%
3822
 
1.1%
6321
 
1.0%
4821
 
1.0%
4520
 
1.0%
7020
 
1.0%
Other values (340)1781
88.7%
ValueCountFrequency (%)
04
0.2%
24
0.2%
34
0.2%
43
 
0.1%
55
0.2%
74
0.2%
96
0.3%
109
0.4%
114
0.2%
128
0.4%
ValueCountFrequency (%)
88171
< 0.1%
38781
< 0.1%
33041
< 0.1%
26271
< 0.1%
25961
< 0.1%
20711
< 0.1%
20491
< 0.1%
19301
< 0.1%
18671
< 0.1%
18631
< 0.1%

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct651
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.0283723
Minimum0
Maximum32658
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:42.025751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q179
median162
Q3325
95-th percentile802
Maximum32658
Range32658
Interquartile range (IQR)246

Descriptive statistics

Standard deviation873.9606814
Coefficient of variation (CV)2.982512152
Kurtosis951.4422218
Mean293.0283723
Median Absolute Deviation (MAD)100
Skewness27.07167542
Sum588694
Variance763807.2726
MonotonicityNot monotonic
2021-09-27T15:04:42.187675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10314
 
0.7%
5514
 
0.7%
6913
 
0.6%
10412
 
0.6%
4212
 
0.6%
5211
 
0.5%
5711
 
0.5%
5611
 
0.5%
3011
 
0.5%
6511
 
0.5%
Other values (641)1889
94.0%
ValueCountFrequency (%)
04
0.2%
16
0.3%
21
 
< 0.1%
33
0.1%
42
 
0.1%
62
 
0.1%
72
 
0.1%
91
 
< 0.1%
123
0.1%
132
 
0.1%
ValueCountFrequency (%)
326581
< 0.1%
99541
< 0.1%
88951
< 0.1%
58091
< 0.1%
55201
< 0.1%
49861
< 0.1%
45201
< 0.1%
37951
< 0.1%
37681
< 0.1%
37111
< 0.1%

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1390
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1527.927825
Minimum4
Maximum191783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:42.369306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile177.8
Q1506
median943
Q31624
95-th percentile3528
Maximum191783
Range191779
Interquartile range (IQR)1118

Descriptive statistics

Standard deviation5041.756848
Coefficient of variation (CV)3.299734952
Kurtosis1033.873191
Mean1527.927825
Median Absolute Deviation (MAD)503
Skewness28.92064342
Sum3069607
Variance25419312.12
MonotonicityNot monotonic
2021-09-27T15:04:42.535740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2926
 
0.3%
7335
 
0.2%
13465
 
0.2%
4695
 
0.2%
4405
 
0.2%
2855
 
0.2%
1465
 
0.2%
5694
 
0.2%
17324
 
0.2%
3224
 
0.2%
Other values (1380)1961
97.6%
ValueCountFrequency (%)
41
< 0.1%
52
0.1%
112
0.1%
151
< 0.1%
172
0.1%
181
< 0.1%
221
< 0.1%
241
< 0.1%
341
< 0.1%
351
< 0.1%
ValueCountFrequency (%)
1917831
< 0.1%
730451
< 0.1%
344771
< 0.1%
343751
< 0.1%
291231
< 0.1%
274951
< 0.1%
273321
< 0.1%
267411
< 0.1%
265781
< 0.1%
186631
< 0.1%

number of tweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.832752613
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:42.681928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile9
Maximum36
Range35
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.667104918
Coefficient of variation (CV)0.6958719196
Kurtosis19.15326634
Mean3.832752613
Median Absolute Deviation (MAD)1
Skewness2.611354534
Sum7700
Variance7.113448644
MonotonicityNot monotonic
2021-09-27T15:04:42.803797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2416
20.7%
1334
16.6%
3328
16.3%
4269
13.4%
5220
11.0%
6172
8.6%
7122
 
6.1%
845
 
2.2%
1036
 
1.8%
936
 
1.8%
Other values (10)31
 
1.5%
ValueCountFrequency (%)
1334
16.6%
2416
20.7%
3328
16.3%
4269
13.4%
5220
11.0%
6172
8.6%
7122
 
6.1%
845
 
2.2%
936
 
1.8%
1036
 
1.8%
ValueCountFrequency (%)
361
 
< 0.1%
311
 
< 0.1%
261
 
< 0.1%
201
 
< 0.1%
191
 
< 0.1%
151
 
< 0.1%
143
 
0.1%
134
 
0.2%
125
 
0.2%
1113
0.6%

price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct886
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.80929731
Minimum7.190000057
Maximum59.40999985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-09-27T15:04:42.947165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7.190000057
5-th percentile9.890000343
Q110.00999991
median10.11999989
Q310.5
95-th percentile34.7019989
Maximum59.40999985
Range52.21999979
Interquartile range (IQR)0.490000089

Descriptive statistics

Standard deviation9.082022792
Coefficient of variation (CV)0.6576745063
Kurtosis7.031590401
Mean13.80929731
Median Absolute Deviation (MAD)0.1800003052
Skewness2.710304085
Sum27742.8783
Variance82.48313799
MonotonicityNot monotonic
2021-09-27T15:04:43.108507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1059
 
2.9%
10.0500001955
 
2.7%
10.0399999654
 
2.7%
10.0600004248
 
2.4%
10.0299997344
 
2.2%
9.9399995837
 
1.8%
9.97000026732
 
1.6%
9.94999980927
 
1.3%
9.96000003827
 
1.3%
9.93000030524
 
1.2%
Other values (876)1602
79.7%
ValueCountFrequency (%)
7.1900000571
 
< 0.1%
7.219999793
0.1%
7.252
0.1%
7.2600002292
0.1%
7.2850000861
 
< 0.1%
7.3000001911
 
< 0.1%
7.3133333521
 
< 0.1%
7.3200001721
 
< 0.1%
7.3299999241
 
< 0.1%
7.3400001531
 
< 0.1%
ValueCountFrequency (%)
59.409999851
< 0.1%
57.150001531
< 0.1%
571
< 0.1%
56.720001221
< 0.1%
561
< 0.1%
55.909999851
< 0.1%
55.613333381
< 0.1%
55.553333281
< 0.1%
55.430001581
< 0.1%
55.196666721
< 0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1499
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0003943241337
Minimum-0.1788092986
Maximum0.2324612465
Zeros211
Zeros (%)10.5%
Negative879
Negative (%)43.8%
Memory size15.8 KiB
2021-09-27T15:04:43.274415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1788092986
5-th percentile-0.03199401991
Q1-0.002001984164
median0
Q30.002483840657
95-th percentile0.03320967566
Maximum0.2324612465
Range0.4112705451
Interquartile range (IQR)0.004485824822

Descriptive statistics

Standard deviation0.02535074752
Coefficient of variation (CV)64.28910977
Kurtosis20.49384753
Mean0.0003943241337
Median Absolute Deviation (MAD)0.002024241281
Skewness0.7639644945
Sum0.7921971847
Variance0.0006426603997
MonotonicityNot monotonic
2021-09-27T15:04:43.421871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0211
 
10.5%
-0.000994058495513
 
0.6%
-0.0009950476319
 
0.4%
-0.0009960387378
 
0.4%
0.0019940636397
 
0.3%
0.0009960387377
 
0.3%
-0.0019900952627
 
0.3%
0.0010040390417
 
0.3%
0.0009950476316
 
0.3%
0.00099703181936
 
0.3%
Other values (1489)1728
86.0%
ValueCountFrequency (%)
-0.17880929861
< 0.1%
-0.17599998141
< 0.1%
-0.17084823851
< 0.1%
-0.15573926331
< 0.1%
-0.1536458671
< 0.1%
-0.133235721
< 0.1%
-0.12969874341
< 0.1%
-0.11269119161
< 0.1%
-0.10902694691
< 0.1%
-0.10200305321
< 0.1%
ValueCountFrequency (%)
0.23246124651
< 0.1%
0.20214456821
< 0.1%
0.19397801351
< 0.1%
0.18753113181
< 0.1%
0.16942058651
< 0.1%
0.16775248081
< 0.1%
0.15844596191
< 0.1%
0.13951939481
< 0.1%
0.13838260051
< 0.1%
0.13467155421
< 0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
no change
1755 
rise
 
129
drop
 
125

Length

Max length9
Median length9
Mean length8.367844699
Min length4

Characters and Unicode

Total characters16811
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdrop
2nd rowrise
3rd rowdrop
4th rowno change
5th rowno change

Common Values

ValueCountFrequency (%)
no change1755
87.4%
rise129
 
6.4%
drop125
 
6.2%

Length

2021-09-27T15:04:43.709139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:04:43.791428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no1755
46.6%
change1755
46.6%
rise129
 
3.4%
drop125
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n3510
20.9%
e1884
11.2%
o1880
11.2%
1755
10.4%
c1755
10.4%
h1755
10.4%
a1755
10.4%
g1755
10.4%
r254
 
1.5%
i129
 
0.8%
Other values (3)379
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15056
89.6%
Space Separator1755
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n3510
23.3%
e1884
12.5%
o1880
12.5%
c1755
11.7%
h1755
11.7%
a1755
11.7%
g1755
11.7%
r254
 
1.7%
i129
 
0.9%
s129
 
0.9%
Other values (2)250
 
1.7%
Space Separator
ValueCountFrequency (%)
1755
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15056
89.6%
Common1755
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n3510
23.3%
e1884
12.5%
o1880
12.5%
c1755
11.7%
h1755
11.7%
a1755
11.7%
g1755
11.7%
r254
 
1.7%
i129
 
0.9%
s129
 
0.9%
Other values (2)250
 
1.7%
Common
ValueCountFrequency (%)
1755
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n3510
20.9%
e1884
11.2%
o1880
11.2%
1755
10.4%
c1755
10.4%
h1755
10.4%
a1755
10.4%
g1755
10.4%
r254
 
1.5%
i129
 
0.8%
Other values (3)379
 
2.3%

Interactions

2021-09-27T15:04:20.053577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:20.174223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:20.298730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:20.428365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:20.548101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:20.666336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:20.785356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:20.909682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.043913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.169232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.288787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.414895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.546182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.686489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.821852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:21.950476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:22.078587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:22.207147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:22.380573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:22.581659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:22.799986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:22.935365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:23.119518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:23.286519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:23.426636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:23.600607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:23.750448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:23.912423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:24.090773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:24.270098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:24.440204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:24.634501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:24.777562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:24.945229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:25.104371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:25.684989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:25.833451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:25.969718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:26.267611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:26.485233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:26.685413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:26.818925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:26.944613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:27.066196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:27.184463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:27.303446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:27.428193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:27.551274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:27.668842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:27.891576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.096870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.222130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.358136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.489942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.609215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.724110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.841199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:28.962279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.083709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.200364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.316072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.431179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.553936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.681391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.803899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:29.921221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:30.033374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:30.167137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:30.297010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:30.427687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:30.553264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:30.750613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:30.916059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:31.045984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:31.183280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:31.315543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:31.441272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:31.565637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:31.715829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:31.873615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:32.013152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:32.151190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:32.296680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:32.433431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:32.573933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:32.722380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:32.871533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.007570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.142366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.280413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.418388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.560652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.689571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.818071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:33.949657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:34.090296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:34.236720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:34.373564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:34.512583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:34.638093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:34.782388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:34.914512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.040820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.162688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.282803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.401646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.529879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.661535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.787033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:35.907281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.085820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.244288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.383895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.514292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.637998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.755391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.869032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:36.989817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:37.116835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:37.237041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:37.350442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-27T15:04:43.887758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-27T15:04:44.106720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-27T15:04:44.324491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-27T15:04:44.547694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-27T15:04:44.723221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-27T15:04:37.599424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-27T15:04:37.939112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
02017-09-29 16:00:00Climate change creates huge opportunities for innovation, not least in transportation. Here’s one example https://t.co/yEPnQFirpo #RunOnLess https://t.co/ypbp5RmDZl How to develop a team of leaders: https://t.co/P0qc7kD55K @virginmobilecan #readbyrichard https://t.co/PSKjZgaGoo Exciting project to modernise Saudi Arabia &amp; welcome tourists to beautiful undiscovered country &amp; stunning landscape https://t.co/jIFLJ10wBe https://t.co/GUmn8qxdKJ @apolloxxxx Thank you for your kind words @RdzSaint Forget one, here’s 65 – not all on entrepreneurship, but all useful for an entrepreneur https://t.co/wTT9njdKlh As a kid one of my heroes was Lawrence of Arabia. Treat to see this restored train he blew up in Saudi Arabia https://t.co/jIFLJ10wBe https://t.co/X2SDLsHQU8 @sarah_robes15 Thank you Sarah, that’s lovely to hear. Next up - #FindingMyVirginity @ErranteMec I’m delighted to hear you enjoyed it. My new autobiography is out next month too, so good timing! #FindingMyVirginity Visited incredible UNESCO World Heritage Site, Mada’in Saleh. Privelige to see this awe-inspiring ancient ruin https://t.co/jIFLJ10wBe https://t.co/CtHQ1Rprsf Quite an experience to be in Saudi Arabia on the day women were given the right to drive for the 1st time https://t.co/jIFLJ10wBe https://t.co/mdFeQz4Q94 Just enjoyed a fascinating visit to Saudi Arabia, a country where great change is taking place step by step https://t.co/jIFLJ10wBerichardbranson140668598552065461110.100000-0.038095drop
12017-09-30 09:30:00My top ten quotes on inspiring leadership: https://t.co/CZ8XdWSm9z https://t.co/5eQHtxwivM Great to see @VirginMedia taking steps to better support disabled employees: https://t.co/l7Xqv7HhH4 https://t.co/7Utka0OpCP Some wonderful insight on what it means to be a modern leader in a @Virgin company: https://t.co/QFxvQ3VrsP #virginfamily #ReadByRichard https://t.co/alkxhgZZMt Enjoyed a fascinating visit to Saudi Arabia, a country where great change is taking place step by step https://t.co/jIFLJ10wBe https://t.co/MeIdSTqiay One area ripe for innovation is trucking. Yes, trucking. This challenge showed how https://t.co/yEPnQFirpo #RunOnLess https://t.co/BLDhkSXBmxrichardbranson23055525721322908510.3833330.028053rise
22017-09-30 16:00:00While I’m excited for the future, it’s interesting listening to reluctant futurist @benhammersley on #FutureVisions https://t.co/OINjUVo8j8 “Take every opportunity that presents itself” – great advice from CEO Merren McArthur: https://t.co/QFxvQ3VrsP #ReadbyRichard #VirginFamily https://t.co/M7KViw1lxQ So proud of my son @sambranson for making this moving documentary on the #BVI community, Help, hope &amp; hurricanes https://t.co/rqaoJNEvugrichardbranson240113832811048310.093334-0.027929drop
32017-10-01 09:30:00Sustainable development advocate @MatsGranryd has joined @thebteamhq: https://t.co/4yZqBeIeUY https://t.co/8wI9eSvGgP While I have given up beef, could smarter grazing actually help reverse global warming? https://t.co/SsRsFltm2r #ReadbyRichard https://t.co/UWJC2xMMZarichardbranson210222103229798210.2666660.017173no change
42017-10-01 16:00:00My top ten quotes on inspiring leadership: https://t.co/CZ8XdWSm9z https://t.co/jIeRYXaHgY Supporting the animal populations of Necker &amp; Moskito Island after the hurricanes: https://t.co/EtiGa2uRPy https://t.co/h7UPgeQCmU Fascinating insight on how gaming can be a force for good from @ian_livingstone and @playmob https://t.co/ICJ7Hm3EMZ #ReadbyRichardrichardbranson210223954891710310.086667-0.017532no change
52017-10-02 09:30:00Prohibition of drugs is harming public health more than it is helping it – it’s time for change https://t.co/DwKyejvmYh https://t.co/iSzEoWhP6k The opioid crisis is affecting all walks of life – we need to act now https://t.co/DwKyejvmYh https://t.co/FrZnXpMeZc The war on drugs has failed and the opioid crisis is a glaring example https://t.co/DwKyejvmYh https://t.co/3oqJqztPdE Thoughts with all affected by the terrible attack in #LasVegas. Keep safe &amp; look out for one another. How do we develop courageous, moral corporate leaders who will build 100% #HumanAtWork organisations? https://t.co/lldBuvG5yF #ReadbyRichard https://t.co/pnLRcklinPrichardbranson0304441245992420510.1500000.006279no change
62017-10-02 16:00:00It’s time to use pragmatism not ideology to tackle North America’s opioid crisis https://t.co/DwKyejvmYh https://t.co/wXvZw5cTBh Why leaders need to create a company culture: https://t.co/zDBN2tsCM6 #ReadbyRichard https://t.co/MehEeMIYMm I'm a huge lover of the great outdoors so thrilled to invest in @SMr_gear on @ABCSharkTank https://t.co/awLb5qPjWL #SharkTank https://t.co/V1bdwW9tRT Sorry to see Monarch go into administration. Brexit among reasons for sad loss of 50-year-old airline https://t.co/sF3YMJBCAz #readbyrichard Excited to invest in 12-year-old Carson's dream on @ABCSharkTank https://t.co/awLb5qPjWL #SharkTank @lockerboard https://t.co/jY6b6Aw220 @DiversabilityC @vmbusiness @VirginStartUp Best of luck with your pitch @haliwell51 Thank you, I thoroughly enjoyed it @LeonardoBittar It would make a tremendous difference Really enjoyed being a guest shark on @ABCSharkTank https://t.co/awLb5qPjWL #SharkTank https://t.co/m9Ebi2BG3P Inspiring entrepreneurs to chase their dreams on @ABCSharkTank https://t.co/awLb5qPjWL #SharkTank https://t.co/Ewhois5KsK Britain sinks from top to bottom of G7 growth table. Very sadly this is just the start of Brexit pain https://t.co/e6hXDWPl2h #readbyrichard My new autobiography #findingmyvirginity is released this week – pre-order it now: https://t.co/NnLINhjH4M https://t.co/iIPqxvrj3R The war on drugs has failed and the opioid crisis is a glaring example https://t.co/DwKyejvmYh https://t.co/fSB9dlidC9richardbranson580881030869123481310.080000-0.006897no change
72017-10-03 09:30:00Read this exclusive excerpt from my new autobiography #FindingMyVirginity https://t.co/UMc4qDzttj https://t.co/BdeGcHdUoS My life is not one long success story – so I hope it makes a good read #FindingMyVirginity https://t.co/UMc4qDzttj https://t.co/xfrjBjOFRn Here’s a sneak preview of my new autobiography #FindingMyVirginity https://t.co/UMc4qDzttj https://t.co/lHgj3rrgOf My top ten quotes on inspiring leadership: https://t.co/CZ8XdWSm9z https://t.co/kIgZEIiYFv The world opens up when we take a positive &amp; optimistic view. We call it #Uptimism: https://t.co/MvbvolUnRw @VirginAustralia https://t.co/WaNPhY1vbD You can’t underestimate the power of positivity and the effect it can have https://t.co/MvbvolUnRw #Uptimism @VirginAustralia https://t.co/cq6UVuLcH6 My 5 top tips for having an Uptimist outlook in life https://t.co/MvbvolUnRw #Uptimism https://t.co/apdvPZ2yDS We’ve always got a smile on. We’ve always got our heads up: https://t.co/MvbvolUnRw @VirginAustralia https://t.co/lLoZcuv9LU We are the Uptimists – here’s to looking up with @VirginAustralia: https://t.co/MvbvolUnRw #Uptimism https://t.co/I82oN3Rq2F Tried to see if a fellow shark liked the water on @ABCSharkTank during @SimpleHabitApp pitch https://t.co/awLb5qPjWL #SharkTankrichardbranson680991019475829921010.0910000.001091no change
82017-10-03 16:00:00I’m still finding my virginity every day – here’s my intro to #FindingMyVirginity https://t.co/UMc4qDzttj https://t.co/sSUnlHFxuy Exciting to see #RunonLess prove available tech could unlock $24 billion for North American trucking: https://t.co/nkxvAyVg1U @cwarroom https://t.co/xUb5E0mCQj Never lose the thrill of trying something for the first time https://t.co/X9WPrDSX1m #FindingMyVirginity https://t.co/Tch3HraS1B @UnleashedSimply Thank you, I hope you enjoy it #findingmyvirginity @sonaysuleyman That’s lovely, thank you for sharing @gemma_rodgers @Virgin Hope you enjoy the evening Gemma #findingmyvirginity 50 years after starting out in business I share the good times and the tough times in #FindingMyVirginity https://t.co/UMc4qDzttj https://t.co/4SlWi6HJCxrichardbranson160444922311179710.1000000.000892no change
92017-10-04 09:30:00How can you compete with other businesses when it comes to innovation? https://t.co/SSeCqcmGgf https://t.co/dxKeqc3crO How do you come up with improvements and solutions for your business? https://t.co/SSeCqcmGgf https://t.co/frvTaC4DbP How have you helped your business innovate? https://t.co/SSeCqcmGgf https://t.co/7sdtcgzYaF How to lead a team to do something that’s never been done before: https://t.co/u6hacrBEji #ReadByRichard https://t.co/UZDNdCwRvZ Gun violence in America, explained in 17 maps and charts https://t.co/fPspJkp03H #readbyrichardrichardbranson0204451234381130510.1000000.000000no change

Last rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
19992021-07-13 09:30:00@Heather92294377 @virgingalactic @SpaceHumanity @omaze Good luck! @FaisalIqbalCric @virgingalactic Yes they do! @jgiegel @virgingalactic Thank you Josh and all the team! #VirginFamily @TheNotoriousMMA Thanks for your kind words @ChrisConwayca @virgingalactic So happy to hear this Chris – thank you for sharing. @ABCSharkTank From the seas to the starsrichardbranson0100005112527640.340000-0.008602no change
20002021-07-13 16:00:00Welcome to space astronauts. https://t.co/Wyzj0nOBgX @virgingalactic #Unity22 https://t.co/9R0ontiS6L @ParisHilton @virgingalactic Thank you for the kind words Paris. @TEDchris Much appreciated Chris. @bertrandpiccard @virgingalactic What kind words Bertrand, means a lot from a fellow adventurer. Thank you. @AinslieBen @virgingalactic Thank you Ben!richardbranson1101014005436149537.759998-0.063956drop
20012021-07-14 16:00:00As always, enjoyed chatting to @StephenAtHome on the @colbertlateshow about the experience of space and how I fulfilled my promise of taking him to space with me: https://t.co/RT3dK5xSXK @virgingalactic #Unity22richardbranson31000113274854133.070000-0.112691drop
20022021-07-15 09:30:00Welcome home @VirginAtlantic. Great to see you back in Terminal 3 at @HeathrowAirport: https://t.co/4QrDLRfDuI #VirginFamily https://t.co/yRpNzbr2YH @virginhyperloop @virgingalactic Fantastic work.richardbranson210101831071175233.029999-0.001210no change
20032021-07-15 16:00:00@VirginUnite Such an important message @VirginUnite Always: a letter to my mum: https://t.co/w7MILeapLv #Unity22 @VirginGalactic https://t.co/Gv9rjSfKASrichardbranson2101012986985740231.740000-0.039055drop
20042021-07-16 09:30:00My world. https://t.co/HpG3ZcCOKW #Unity22 @HollyBranson @virgingalactic https://t.co/DYYdYj81NA Today, @TheElders are reflecting on Nelson Mandela’s legacy and looking at its relevance to current challenges. Join them during a special live online event (3pm BST / 10am EDT) as they address our global need for a #StateOfHope: https://t.co/iF21HLAiUn https://t.co/kPk3Vu5Y9o Featuring Seedlip founder Ben Branson (no relation), the next @VirginStartUp will give you the inside track on starting and scaling a food and drink business. Brilliant. Tickets are free but limited, so sign up here: https://t.co/gDTGn6pm2w https://t.co/EFyAsLFUOyrichardbranson4203234303674855332.5400010.025205no change
20052021-07-18 09:30:00An unforgettable moment. #Unity22 @virgingalactic https://t.co/8WnseIXTtRrichardbranson11010046481510141130.480001-0.014655no change
20062021-07-19 09:30:00The moment we saw Earth from space... @virgingalactic #Unity22 https://t.co/LTHSKqABAj Wonderful to hear the story behind @VyableB - a business launched with support from @VirginStartUp: https://t.co/wHiuI5a5zh https://t.co/x2FWFQ9uoe @HallaTomas @HollyBranson Thanks Halla - very happy I did! @VirginActiveOz Thank you team @VirginBalloons Thanks team @Virgin Thanks to all the #VirginFamily @VirginAtlantic Thank you team @VirginLimitedEd Thanks! @VirginActiveUK Thank you to all the team @VirginMENA Thank you team @virginhyperloop Thanks team @jgiegel Thanks Josh - and for the seat! @nathanielpeat Happy belated birthday to you too! @VirginHotelsLV Thanks team! @VirginVoyages Thanks to all the crew @RoyaMahboob Thanks very much Roya! @virgingalactic Thanks team @Astro_Cady @virgingalactic Many thanks Cady @WildAid Thanks to all the team for all your wonderful work @HollyBranson Thanks Holly, love you toorichardbranson32021144069881422029.450001-0.070000drop
20072021-07-19 16:00:00Virgin Mobile Canada has become @virginplus – reflecting its evolving service offering: https://t.co/YPnUK2HILc #VirginFamily https://t.co/O9CunM6oQhrichardbranson1101014629292132.4000020.100170rise
20082021-07-20 16:00:00@virginplus Brilliant! @virginhotels @VirginVoyages @belvederevodka Love the teamwork. @VirginHotels @VirginVoyages Well done @blueorigin, @jeffbezos, Mark, Wally and Oliver. Impressive! Very best to all the crew from me and all the team at @virgingalacticrichardbranson500000422104714275332.0299990.030235rise